Trauma-Associated Page Kidney: A Systematic Review and Data Analysis

---

Required Libraries

library(dplyr)
library(knitr)
library(kableExtra)
library(stringr)
library(tidyr)
library(glue)
library(ggplot2)

Data Loading and Preprocessing

# Load the raw dataset
df <- read.csv("data/trauma-page-kidney-table.csv") %>% 
    select(-review.1, -review.2) 

# Identify excluded manuscripts for reporting
exdf <- df %>% filter(!str_starts(include, "y"))

# Process included manuscripts with comprehensive data cleaning
df <- df %>% filter(str_starts(include, "y")) %>%
    select(-pdf, -include) %>%
    mutate(
        reference = as.factor(reference),
        year = as.integer(year),
        age = as.numeric(age),
        gender = factor(gender),
        pmh = as.character(pmh),
        onset.y = as.numeric(onset.y),
        mechanism = factor(mechanism),
        ss = as.character(ss),
        hypertension = if_else(hypertension == "y", TRUE, FALSE),
        ua = factor(ua),
        grade = factor(grade, levels = sort(unique(grade), na.last = TRUE)),
        size.cm = as.numeric(size.cm),
        page.type = factor(page.type),
        laterality = factor(laterality),
        treatment = as.character(treatment),
        fu.status = if_else(str_trim(fu.status) == "s", TRUE, NA),
        fu.y = as.numeric(fu.y)
  )

# Clean urinalysis results for standardized reporting
df <- df %>%
  mutate(
    ua_clean = str_to_lower(ua),
    ua_result = case_when(
      ua_clean %in% c("hematuria", "trace blood") ~ "Positive",
      ua_clean == "negative" ~ "Negative",
      TRUE ~ NA_character_  # preserve NA when UA not done
    )
  )

# Standardize laterality reporting
df <- df %>%
  mutate(
    laterality = tolower(as.character(laterality)),
    laterality = gsub(",", "", laterality),
    laterality = str_trim(laterality),
    laterality = factor(laterality)
  )

Dataset Processing Summary:

• Total references processed: 35
• References excluded: 11
• References included in analysis: 24
• Unique studies analyzed: 23
• Individual patients: 24

Study Selection and Exclusions

# Process exclusion data
exdf <- exdf %>% 
  select(reference, include) %>%
  mutate(
    reference = as.factor(reference),
    include = as.factor(include)
  )

# Create exclusion summary table
exclusion_summary <- as.data.frame(table(exdf$include))
colnames(exclusion_summary) <- c("Reason for Exclusion", "Number of References")

exclusion_summary %>%
  kbl(caption = "Summary of Study Exclusions",
      align = "lc",
      booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
                full_width = FALSE,
                position = "center")

Summary of Study Exclusions

Reason for Exclusion	Number of References
duplicate	1
not-available	6
path	2
patient level	2

Total excluded references: 11

Excluded references: Arai_1981, Barns_2022, Benendo-Kapuscinska_1999, El Madhoun_2009, Estrada_2019, Hoshiyama_2009, Oliveira_2002, Scott_1976, Shome_2002, Smyth_2012, van Ahlen_1987

Publication Patterns

# Page Kidney Systematic Review: Publication Year Analysis
# Load required libraries
years <- df$year
# Basic descriptive statistics
basic_stats <- data.frame(
  Statistic = c("Total papers", "Min year", "Max year", "Year span", "Mean year", "Median year"),
  Value = c(
    length(years),
    min(years),
    max(years),
    max(years) - min(years),
    round(mean(years), 1),
    median(years)
  )
)

print("=== BASIC STATISTICS ===")

[1] "=== BASIC STATISTICS ==="

kable(basic_stats, caption = "Basic Publication Statistics")

Basic Publication Statistics

Statistic	Value
Total papers	24
Min year	1969
Max year	2024
Year span	55
Mean year	2009
Median year	2016

# Frequency distribution by decade
decade_freq <- years %>%
  sapply(function(x) floor(x/10) * 10) %>%
  table() %>%
  as.data.frame()
names(decade_freq) <- c("Decade", "Papers")
decade_freq$Decade <- paste0(decade_freq$Decade, "s")

print("=== FREQUENCY BY DECADE ===")

[1] "=== FREQUENCY BY DECADE ==="

kable(decade_freq, caption = "Publications by Decade")

Publications by Decade

Decade	Papers
1960s	1
1970s	1
1990s	3
2000s	4
2010s	9
2020s	6

# Era analysis
era_breaks <- c(1969, 2000, 2010, 2020, 2025)
era_labels <- c("Pre-2000", "2000-2009", "2010-2019", "2020-2024")

era_analysis <- data.frame(
  Era = era_labels,
  Papers = c(
    sum(years < 2000),
    sum(years >= 2000 & years < 2010),
    sum(years >= 2010 & years < 2020),
    sum(years >= 2020)
  )
)
era_analysis$Percentage <- round((era_analysis$Papers / length(years)) * 100, 1)
era_analysis$Rate_per_year <- c(
  era_analysis$Papers[1] / 31,  # 1969-1999 = 31 years
  era_analysis$Papers[2] / 10,  # 2000-2009 = 10 years
  era_analysis$Papers[3] / 10,  # 2010-2019 = 10 years
  era_analysis$Papers[4] / 5    # 2020-2024 = 5 years
)
era_analysis$Rate_per_year <- round(era_analysis$Rate_per_year, 2)

print("=== ERA DISTRIBUTION ===")

[1] "=== ERA DISTRIBUTION ==="

kable(era_analysis, caption = "Publications by Era with Rates")

Publications by Era with Rates

Era	Papers	Percentage	Rate_per_year
Pre-2000	5	21	0.16
2000-2009	4	17	0.40
2010-2019	9	38	0.90
2020-2024	6	25	1.20

# Recent years analysis (2015-2024)
recent_years <- 2015:2024
recent_counts <- sapply(recent_years, function(x) sum(years == x))
recent_analysis <- data.frame(
  Year = recent_years,
  Papers = recent_counts
)

print("=== RECENT PUBLICATION PATTERN (2015-2024) ===")

[1] "=== RECENT PUBLICATION PATTERN (2015-2024) ==="

kable(recent_analysis, caption = "Year-by-Year Publications (2015-2024)")

Year-by-Year Publications (2015-2024)

Year	Papers
2015	1
2016	1
2017	2
2018	2
2019	1
2020	1
2021	2
2022	0
2023	1
2024	2

# Visualization: Publications over time
# Create a complete time series
year_counts <- table(factor(years, levels = min(years):max(years)))
year_df <- data.frame(
  Year = as.numeric(names(year_counts)),
  Papers = as.numeric(year_counts)
)

# Plot 1: Publications by year
p1 <- ggplot(year_df, aes(x = Year, y = Papers)) +
  geom_col(fill = "steelblue", alpha = 0.7) +
  geom_smooth(method = "loess", se = TRUE, color = "red", linetype = "dashed") +
  labs(
    title = "Page Kidney Publications by Year (1969-2024)",
    x = "Publication Year",
    y = "Number of Publications",
    caption = "Trend line shows increasing publication activity over time"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 14, face = "bold"),
    axis.text.x = element_text(angle = 45, hjust = 1)
  ) +
  scale_x_continuous(breaks = seq(1970, 2024, 5))

print(p1)

# Plot 2: Publications by era
p2 <- ggplot(era_analysis, aes(x = Era, y = Papers)) +
  geom_col(fill = "darkgreen", alpha = 0.7) +
  geom_text(aes(label = paste0(Papers, " (", Percentage, "%)")), 
            vjust = -0.5, size = 3.5) +
  labs(
    title = "Page Kidney Publications by Era",
    x = "Time Period",
    y = "Number of Publications",
    caption = "Percentages show proportion of total publications"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 14, face = "bold"),
    axis.text.x = element_text(angle = 45, hjust = 1)
  ) +
  ylim(0, max(era_analysis$Papers) * 1.15)

print(p2)

# Summary statistics for text
cat("\n=== KEY FINDINGS FOR MANUSCRIPT ===\n")

=== KEY FINDINGS FOR MANUSCRIPT ===

cat("• Research spans", max(years) - min(years), "years (", min(years), "-", max(years), ")\n")

• Research spans 55 years ( 1969 - 2024 )

cat("• Mean publication year:", round(mean(years), 1), "\n")

• Mean publication year: 2009 

cat("• Median publication year:", median(years), "\n")

• Median publication year: 2016 

cat("•", round(sum(years >= 2010) / length(years) * 100, 1), "% of papers published in last 15 years (2010-2024)\n")

• 62 % of papers published in last 15 years (2010-2024)

cat("• Publication rate increased from", round(sum(years >= 2000 & years < 2010) / 10, 1), 
    "papers/year (2000s) to", round(sum(years >= 2020) / 5, 1), "papers/year (2020s)\n")

• Publication rate increased from 0.4 papers/year (2000s) to 1.2 papers/year (2020s)

cat("• Peak activity in 2010s with", sum(years >= 2010 & years < 2020), "publications\n")

• Peak activity in 2010s with 9 publications

cat("• Most productive individual years: 2017, 2018, 2021, 2024 (≥2 papers each)\n")

• Most productive individual years: 2017, 2018, 2021, 2024 (≥2 papers each)

# Create summary table for manuscript
manuscript_summary <- data.frame(
  Metric = c(
    "Total publications",
    "Publication span (years)",
    "Mean publication year",
    "Median publication year", 
    "Publications 2010-2024 (%)",
    "Publication rate 2000-2009 (per year)",
    "Publication rate 2020-2024 (per year)"
  ),
  Value = c(
    length(years),
    paste0(max(years) - min(years), " (", min(years), "-", max(years), ")"),
    round(mean(years), 1),
    median(years),
    paste0(sum(years >= 2010), " (", round(sum(years >= 2010) / length(years) * 100, 1), "%)"),
    round(sum(years >= 2000 & years < 2010) / 10, 1),
    round(sum(years >= 2020) / 5, 1)
  )
)

cat("\n=== MANUSCRIPT SUMMARY TABLE ===\n")

=== MANUSCRIPT SUMMARY TABLE ===

kable(manuscript_summary, caption = "Publication Characteristics Summary")

Publication Characteristics Summary

Metric	Value
Total publications	24
Publication span (years)	55 (1969-2024)
Mean publication year	2009.3
Median publication year	2015.5
Publications 2010-2024 (%)	15 (62.5%)
Publication rate 2000-2009 (per year)	0.4
Publication rate 2020-2024 (per year)	1.2

Patient Demographics and Baseline Characteristics

Age Distribution Analysis

# Comprehensive age statistics using your original approach
describe_age <- function(x) {
  sprintf(
    "The median age at presentation was %.0f years (IQR: %.0f–%.0f, range: %.0f–%.0f). The mean age was %.1f ± %.1f years.",
    median(x, na.rm = TRUE),
    quantile(x, 0.25, na.rm = TRUE),
    quantile(x, 0.75, na.rm = TRUE),
    min(x, na.rm = TRUE),
    max(x, na.rm = TRUE),
    mean(x, na.rm = TRUE),
    sd(x, na.rm = TRUE)
  )
}

age_description <- describe_age(df$age)

# Create publication-quality age distribution plot
ggplot(df, aes(x = age)) +
  geom_histogram(aes(y = after_stat(density)), binwidth = 5, 
                 fill = "steelblue", color = "black", alpha = 0.7) +
  geom_density(color = "darkblue", linewidth = 1.2) +
  labs(
    title = "Age Distribution at Page Kidney Presentation",
    subtitle = paste0("n = ", sum(!is.na(df$age)), " patients"),
    x = "Age (years)",
    y = "Density"
  )

# Summary statistics table
age_stats <- data.frame(
  Statistic = c("n", "Median (IQR)", "Mean ± SD", "Range"),
  Value = c(
    sum(!is.na(df$age)),
    paste0(median(df$age, na.rm = TRUE), " (", 
           quantile(df$age, 0.25, na.rm = TRUE), "–", 
           quantile(df$age, 0.75, na.rm = TRUE), ")"),
    paste0(round(mean(df$age, na.rm = TRUE), 1), " ± ", 
           round(sd(df$age, na.rm = TRUE), 1)),
    paste0(min(df$age, na.rm = TRUE), "–", max(df$age, na.rm = TRUE))
  )
)

age_stats %>%
  kbl(caption = "Age Distribution Summary",
      align = "lc") %>%
  kable_styling(bootstrap_options = c("striped", "hover"),
                full_width = FALSE)

Age Distribution Summary

Statistic	Value
n	24
Median (IQR)	24.5 (16.75–49.5)
Mean ± SD	34.2 ± 21.7
Range	9–89

Age Characteristics: The median age at presentation was 24 years (IQR: 17–50, range: 9–89). The mean age was 34.2 ± 21.7 years.

Gender Distribution

# Gender analysis maintaining your original approach
gender_counts <- table(df$gender)
total_patients <- nrow(df)

Gender Distribution: There was a marked male predominance in this cohort, with 23 males (95.8%) and 1 females (4.2%). This distribution is consistent with the literature on trauma-related renal injuries and reflects higher rates of trauma exposure in males.

Clinical Characteristics

Past Medical History Analysis

# Your original PMH processing approach with enhanced formatting
pmh_vec <- df$pmh

pmh_clean <- data.frame(id = seq_along(pmh_vec), pmh = pmh_vec) %>%
  mutate(pmh = str_to_lower(pmh)) %>%
  separate_rows(pmh, sep = ";\\s*") %>%
  mutate(pmh = str_trim(pmh)) %>%
  filter(pmh != "none" & pmh != "")

# Handle compound phrases that should yield 2+ categories
manual_split <- pmh_clean %>%
  filter(
    str_detect(pmh, "dm, polyneuropathy") |
    str_detect(pmh, "renal/pancreas") |
    str_detect(pmh, "sleep apnea.*htn")
  ) %>%
  mutate(category = case_when(
    str_detect(pmh, "dm, polyneuropathy") ~ list(c("Diabetes", "Neuropathy")),
    str_detect(pmh, "renal/pancreas") ~ list(c("Renal transplant", "Pancreas transplant")),
    str_detect(pmh, "sleep apnea.*htn") ~ list(c("Sleep apnea", "Hypertension"))
  )) %>%
  unnest_longer(category)

pmh_main <- pmh_clean %>%
  filter(!pmh %in% manual_split$pmh)

# categorization logic
pmh_main <- pmh_main %>%
  mutate(category = case_when(
    str_detect(pmh, "pancreas transplant|renal/pancreas|pancreas") ~ "Pancreas transplant",
    str_detect(pmh, "renal txp|renal transplant") ~ "Renal transplant",
    str_detect(pmh, "htn|hypertension|hctz|losartan|amlodipine") ~ "Hypertension",
    str_detect(pmh, "dm|diabetes") ~ "Diabetes",
    str_detect(pmh, "cad") ~ "Coronary artery disease",
    str_detect(pmh, "chronic renal|renal insufficiency|crf") ~ "Chronic renal insufficiency",
    str_detect(pmh, "glomerulonephritis") ~ "Glomerulonephritis",
    str_detect(pmh, "absent|congenital|atrophic") ~ "Congenital kidney anomaly",
    str_detect(pmh, "seizure") ~ "Seizure disorder",
    str_detect(pmh, "asthma") ~ "Asthma",
    str_detect(pmh, "sleep apnea|cpap") ~ "Sleep apnea",
    str_detect(pmh, "polyneuropathy|neuropathy") ~ "Neuropathy",
    str_detect(pmh, "fracture") ~ "Fracture",
    str_detect(pmh, "marijuana") ~ "Substance use",
    TRUE ~ "Other"
  ))

pmh_all <- bind_rows(pmh_main, manual_split)

# Get total number of patients
total_patients <- n_distinct(df$pmh)

# Create PMH summary table
pmh_summary <- pmh_all %>%
  count(category, name = "n") %>%
  arrange(desc(n)) %>%
  mutate(Percentage = round(100 * n / total_patients, 1))

# Professional visualization
ggplot(pmh_summary, aes(x = reorder(category, n), y = n)) +
  geom_col(fill = "#4C72B0") +
  coord_flip() +
  labs(
    title = "Distribution of Past Medical History",
    subtitle = paste0("Based on ", total_patients, " total patients"),
    x = "Medical Condition",
    y = "Number of Patients"
  )

# Summary table
pmh_summary %>%
  select(`Medical Condition` = category, n, `Percentage (%)` = Percentage) %>%
  kbl(caption = "Past Medical History Distribution",
      align = "lcc") %>%
  kable_styling(bootstrap_options = c("striped", "condensed"), 
                full_width = FALSE)

Past Medical History Distribution

Medical Condition	n	Percentage (%)
Renal transplant	4	36.4
Diabetes	3	27.3
Congenital kidney anomaly	2	18.2
Hypertension	2	18.2
Asthma	1	9.1
Chronic renal insufficiency	1	9.1
Coronary artery disease	1	9.1
Fracture	1	9.1
Neuropathy	1	9.1
Pancreas transplant	1	9.1
Seizure disorder	1	9.1
Sleep apnea	1	9.1
Substance use	1	9.1

Past Medical History Summary:

The most common comorbidities included hypertension (n = 2), diabetes (n = 3), and renal transplant (n = 4). Less frequent conditions included congenital kidney anomalies, chronic renal insufficiency, and sleep apnea.

Several patients had multiple comorbidities. Compound diagnoses such as diabetic neuropathy or sleep apnea–induced hypertension were classified under multiple categories to reflect their clinical relevance.

Time from Injury to Diagnosis

# onset analysis
onset_stats <- list(
  summary = summary(df$onset.y),
  mean = mean(df$onset.y, na.rm = TRUE),
  sd = sd(df$onset.y, na.rm = TRUE),
  quantiles = quantile(df$onset.y, probs = c(0.25, 0.5, 0.75), na.rm = TRUE)
)

# histogram
ggplot(df, aes(x = onset.y)) +
  geom_histogram(binwidth = 0.25, fill = "#FF8C00", color = "black") +
  labs(
    title = "Time from Injury to Diagnosis of Page Kidney",
    subtitle = paste0("n = ", sum(!is.na(df$onset.y)), " patients with reported onset time"),
    x = "Time to Diagnosis (years)",
    y = "Number of Patients"
  )

# log-scaled plot
ggplot(df, aes(x = onset.y)) +
  geom_histogram(binwidth = 0.25, fill = "steelblue", color = "black") +
  scale_x_log10() +
  labs(
    title = "Log-Scaled Time to Diagnosis of Page Kidney",
    subtitle = "Better visualization of the distribution spread",
    x = "Time to Diagnosis (log years)",
    y = "Number of Patients"
  )

# Summary statistics table
onset_summary_table <- data.frame(
  Statistic = c("n", "Median", "IQR", "Mean ± SD", "Range"),
  `Time (years)` = c(
    sum(!is.na(df$onset.y)),
    round(median(df$onset.y, na.rm = TRUE), 2),
    paste0(round(quantile(df$onset.y, 0.25, na.rm = TRUE), 2), "–", 
           round(quantile(df$onset.y, 0.75, na.rm = TRUE), 2)),
    paste0(round(mean(df$onset.y, na.rm = TRUE), 2), " ± ", 
           round(sd(df$onset.y, na.rm = TRUE), 2)),
    paste0(round(min(df$onset.y, na.rm = TRUE), 3), "–", 
           round(max(df$onset.y, na.rm = TRUE), 1))
  ),
  `Time (days)` = c(
    "",
    round(median(df$onset.y, na.rm = TRUE) * 365),
    paste0(round(quantile(df$onset.y, 0.25, na.rm = TRUE) * 365), "–", 
           round(quantile(df$onset.y, 0.75, na.rm = TRUE) * 365)),
    paste0(round(mean(df$onset.y, na.rm = TRUE) * 365), " ± ", 
           round(sd(df$onset.y, na.rm = TRUE) * 365)),
    paste0(round(min(df$onset.y, na.rm = TRUE) * 365), "–", 
           round(max(df$onset.y, na.rm = TRUE) * 365))
  )
)

onset_summary_table %>%
  kbl(caption = "Time from Injury to Page Kidney Diagnosis",
      align = "lcc") %>%
  kable_styling(bootstrap_options = c("striped", "hover"),
                full_width = FALSE)

Time from Injury to Page Kidney Diagnosis

Statistic	Time..years.	Time..days.
n	23
Median	0.04	15
IQR	0.01–0.67	5–243
Mean ± SD	1 ± 2.23	367 ± 813
Range	0–9	0–3285

Time from Injury to Diagnosis:

The time from injury to diagnosis of Page kidney varied substantially among patients. The median time to diagnosis was approximately 0.04 years (~15 days), with an interquartile range (IQR) of 0.01 to 0.67 years.

The mean time to diagnosis was 1 ± 2.23 years, and the range extended from 0 to 9 years.

This distribution reflects both acute presentations (diagnosed within days) and chronic or delayed diagnoses (up to several years), emphasizing the importance of clinical vigilance following trauma or renal interventions.

Mechanism of Injury

# mechanism analysis
total_patients <- nrow(df)
mechanism_summary <- as.data.frame(table(df$mechanism))
colnames(mechanism_summary) <- c("Mechanism", "Count")

mechanism_summary <- mechanism_summary %>%
  mutate(`Percentage (%)` = round(100 * Count / total_patients, 1))

mechanism_summary %>%
  kbl(caption = "Mechanism of Injury Leading to Page Kidney",
      align = "lcc") %>%
  kable_styling(bootstrap_options = c("striped", "hover"),
                full_width = FALSE)

Mechanism of Injury Leading to Page Kidney

Mechanism	Count	Percentage (%)
blunt	12	50
fall	6	25
mvc	6	25

# Professional bar plot
ggplot(mechanism_summary, aes(x = reorder(Mechanism, -Count), y = Count)) +
  geom_col(fill = "#4C72B0") +
  labs(
    title = "Mechanism of Injury Leading to Page Kidney",
    x = "Mechanism of Injury",
    y = "Number of Cases"
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Mechanism of Injury:

Among the patients included, the most common mechanisms of injury were blunt (12 cases, 50%), followed by fall (6 cases, 25%) and mvc (6 cases, 25%).

Clinical Presentation and Symptoms

# symptom processing
df <- df %>%
  mutate(
    ss_combined = ifelse(hypertension, 
                         ifelse(ss == "" | is.na(ss), "hypertension", paste(ss, "hypertension", sep = "; ")), 
                         ss)
  )

df <- df %>%
  mutate(
    ua_clean = str_to_lower(ua),
    ss_combined = ifelse(
      ua_clean == "hematuria" & !str_detect(str_to_lower(ss_combined), "hematuria"),
      ifelse(is.na(ss_combined) | ss_combined == "", "hematuria", paste(ss_combined, "hematuria", sep = "; ")),
      ss_combined
    )
  )

# Create ID and clean the combined symptom column
ss_clean <- data.frame(id = seq_along(df$ss_combined), ss = df$ss_combined) %>%
  mutate(ss = str_to_lower(ss)) %>%
  mutate(ss = str_replace_all(ss, ",", ";")) %>%
  separate_rows(ss, sep = ";\\s*") %>%
  mutate(ss = str_trim(ss)) %>%
  filter(ss != "") %>%
  distinct(id, ss)  

# symptom categorization
ss_clean <- ss_clean %>%
  mutate(symptom = case_when(
    str_detect(ss, "flank pain") ~ "Flank pain",
    str_detect(ss, "abdominal pain") ~ "Abdominal pain",
    str_detect(ss, "back pain") ~ "Back pain",
    str_detect(ss, "chest pain") ~ "Chest Pain",
    str_detect(ss, "nausea") ~ "Nausea",
    str_detect(ss, "vomiting") ~ "Vomiting",
    str_detect(ss, "hematemesis") ~ "Hematemesis",
    str_detect(ss, "headache") ~ "Headache",
    str_detect(ss, "syncope") ~ "Syncope",
    str_detect(ss, "hematuria") ~ "Hematuria",
    str_detect(ss, "palpitations|palpations") ~ "Palpitations",
    str_detect(ss, "oligou?ria") ~ "Oliguria",
    str_detect(ss, "anuria") ~ "Anuria",
    str_detect(ss, "fatigue") ~ "Fatigue",
    str_detect(ss, "distension") ~ "Abdominal distension",
    str_detect(ss, "ecchymosis") ~ "Ecchymosis",
    str_detect(ss, "hypertension") ~ "Hypertension",
    str_detect(ss, "aki") ~ "Acute kidney injury",
    str_detect(ss, "asymptomatic") ~ "Asymptomatic",
    TRUE ~ str_to_title(ss)
  ))

symptom_summary <- ss_clean %>%
  count(symptom, sort = TRUE) %>%
  mutate(Percentage = round(100 * n / n_distinct(ss_clean$id), 1))

symptom_summary %>%
  kbl(caption = "Presenting Symptoms and Signs",
      col.names = c("Symptom", "Count", "Percentage (%)"),
      align = "lcc") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
                full_width = FALSE)

Presenting Symptoms and Signs

Symptom	Count	Percentage (%)
Hypertension	13	92.9
Flank pain	8	57.1
Hematuria	7	50.0
Abdominal pain	4	28.6
Back pain	2	14.3
Headache	2	14.3
Abdominal distension	1	7.1
Asymptomatic	1	7.1
Chest Pain	1	7.1
Ecchymosis	1	7.1
Fatigue	1	7.1
Palpitations	1	7.1
Vomiting	1	7.1

# Professional symptom plot
ggplot(symptom_summary, aes(x = reorder(symptom, n), y = n)) +
  geom_col(fill = "#D55E00") +
  geom_text(aes(label = paste0(Percentage, "%")), hjust = -0.1) +
  coord_flip() +
  labs(
    title = "Frequency of Presenting Symptoms and Signs",
    subtitle = "Clinical features at presentation",
    x = "Symptom/Sign",
    y = "Number of Patients"
  ) +
  ylim(0, max(symptom_summary$n) * 1.15)

Clinical Presentation:

Patients with Page kidney presented with a diverse range of clinical symptoms and signs. The most common presenting feature was Hypertension, reported in 13 patient(s) (92.9%). This was followed by Flank pain (8 patients, 57.1%) and Hematuria (7 patients, 50%).

Notably, 13 patients (92.9%) presented with hypertension as a clinical feature, emphasizing the hemodynamic impact of retroperitoneal compression. Additionally, 1 patient(s) were asymptomatic at diagnosis.

Diagnostic Findings

Urinalysis Results

ua_summary <- df %>%
  mutate(ua_result = case_when(
    is.na(ua) ~ "Missing",
    str_to_lower(ua) %in% c("hematuria") ~ "Positive",
    str_to_lower(ua) %in% c("negative") ~ "Negative",
    str_detect(str_to_lower(ua), "trace") ~ "Positive",
    TRUE ~ "Other"
  )) %>%
  count(ua_result, name = "n") %>%
  mutate(percent = round(100 * n / sum(n), 1))

ua_summary %>%
  kbl(caption = "Urinalysis Results Distribution",
      col.names = c("Urinalysis Result", "n", "Percentage (%)"),
      align = "lcc") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
                full_width = FALSE)

Urinalysis Results Distribution

Urinalysis Result	n	Percentage (%)
Missing	10	42
Negative	6	25
Positive	8	33

Urinalysis Findings: Urinalysis results were available in 14 of 24 cases. Among tested patients, hematuria (positive results) was found in 8 cases (33.3%), while 6 cases (25%) had negative urinalysis.

Injury Grade Assessment

grade_summary <- df %>%
  count(grade, name = "n", drop = FALSE) %>%
  mutate(
    Grade = ifelse(is.na(grade), "Missing", as.character(grade)),
    Percentage = round(100 * n / nrow(df), 1)
  ) %>%
  select(Grade, n, Percentage)

grade_summary %>%
  kbl(caption = "Distribution of AAST Kidney Injury Grades",
      align = "lcc") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                full_width = FALSE)

Distribution of AAST Kidney Injury Grades

Grade	n	Percentage
II	1	4.2
IV	1	4.2
V	1	4.2
Missing	21	87.5

# Professional grade visualization
ggplot(grade_summary, aes(x = reorder(Grade, -n), y = n, fill = Grade)) +
  geom_col(width = 0.6, color = "black") +
  geom_text(aes(label = paste0(n, " (", Percentage, "%)")), 
            vjust = -0.5, size = 4) +
  labs(
    title = "AAST Kidney Injury Grade Distribution",
    subtitle = "Injury severity classification",
    x = "AAST Grade",
    y = "Number of Patients"
  ) +
  theme(legend.position = "none")

Injury Grading: Among the reported cases, AAST kidney injury grades were documented in a minority of patients. Grade V injuries were the most frequently reported (1, 4.2%), followed by grade IV (1, 4.2%) and grade II (1, 4.2%). However, the majority of cases (21, 87.5%) did not specify an AAST grade, limiting formal stratification by injury severity. This reflects the variability in reporting standards across case reports and underscores the need for consistent documentation of injury grading.

Page Kidney Imaging Characteristics

Size of Perinephric Fluid Collections

size_summary <- df %>%
  summarise(
    median = median(size.cm, na.rm = TRUE),
    iqr_low = quantile(size.cm, 0.25, na.rm = TRUE),
    iqr_high = quantile(size.cm, 0.75, na.rm = TRUE),
    min = min(size.cm, na.rm = TRUE),
    max = max(size.cm, na.rm = TRUE),
    mean = mean(size.cm, na.rm = TRUE),
    sd = sd(size.cm, na.rm = TRUE),
    missing = sum(is.na(size.cm)),
    total = n()
  )

# Size distribution histogram
ggplot(df %>% filter(!is.na(size.cm)), aes(x = size.cm)) +
  geom_histogram(bins = 12, fill = "#20B2AA", alpha = 0.8, color = "white") +
  geom_vline(aes(xintercept = median(size.cm, na.rm = TRUE)), 
             color = "red", linetype = "dashed", linewidth = 1) +
  labs(
    title = "Distribution of Fluid Collection Sizes",
    subtitle = paste0("n = ", sum(!is.na(df$size.cm)), " patients with reported measurements"),
    x = "Collection Size (cm)",
    y = "Number of Patients"
  )

# Size summary table
size_stats_table <- data.frame(
  Statistic = c("n", "Median (IQR)", "Mean ± SD", "Range", "Missing"),
  Value = c(
    sum(!is.na(df$size.cm)),
    paste0(size_summary$median, " (", size_summary$iqr_low, "–", size_summary$iqr_high, ")"),
    paste0(round(size_summary$mean, 1), " ± ", round(size_summary$sd, 1)),
    paste0(size_summary$min, "–", size_summary$max),
    paste0(size_summary$missing, " (", round(100 * size_summary$missing / size_summary$total, 1), "%)")
  )
)

size_stats_table %>%
  kbl(caption = "Perinephric Fluid Collection Size Statistics",
      align = "lc") %>%
  kable_styling(bootstrap_options = c("striped", "hover"),
                full_width = FALSE)

Perinephric Fluid Collection Size Statistics

Statistic	Value
n	13
Median (IQR)	11 (7.4–12)
Mean ± SD	10.7 ± 4
Range	4–18
Missing	11 (45.8%)

Fluid Collection Size Analysis:

The size of the perinephric fluid collections on imaging at presentation varied widely. The median size was 11 cm (IQR: 7.4–12 cm), with a mean of 10.7 ± 4 cm. The smallest collection measured 4 cm, while the largest was 18 cm. Notably, size data was not reported in 11 out of 24 cases (45.8%).

Type of Fluid Collection

page_type_summary <- df %>%
  mutate(page_type_clean = ifelse(is.na(page.type), "Missing", as.character(page.type))) %>%
  count(page_type_clean, name = "n") %>%
  mutate(Percentage = round(100 * n / sum(n), 1))

page_type_summary %>%
  kbl(caption = "Type of Perinephric Fluid Collection",
      col.names = c("Collection Type", "n", "Percentage (%)"),
      align = "lcc") %>%
  kable_styling(bootstrap_options = c("striped", "hover"),
                full_width = FALSE)

Type of Perinephric Fluid Collection

Collection Type	n	Percentage (%)
Missing	1	4.2
hematoma	21	87.5
lymphocele	1	4.2
urinoma	1	4.2

# Visualization
ggplot(page_type_summary, aes(x = reorder(page_type_clean, n), y = n)) +
  geom_col(fill = "#800000", alpha = 0.8) +
  geom_text(aes(label = paste0(n, " (", Percentage, "%)")), 
            hjust = -0.1, size = 4) +
  coord_flip() +
  labs(
    title = "Distribution of Fluid Collection Types",
    x = "Collection Type",
    y = "Number of Cases"
  ) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.15)))

Fluid Collection Types:

The type of perinephric fluid collection leading to Page kidney was most commonly a hematoma, accounting for 87.5% of cases (21 of 24 patients). Other etiologies included:

• Urinoma: 1 case(s) (4.2%)
• Lymphocele: 1 case(s) (4.2%)
• Missing Data: 1 case(s) (4.2%)

These findings underscore that hematoma is the predominant cause of Page kidney across reported cases.

Laterality Distribution

laterality_table <- df %>%
  count(laterality) %>%
  mutate(Percentage = round(100 * n / sum(n), 1))

laterality_table %>%
  kbl(caption = "Distribution of Laterality in Page Kidney Cases",
      align = "lcc") %>%
  kable_styling(bootstrap_options = c("striped", "hover"),
                full_width = FALSE)

Distribution of Laterality in Page Kidney Cases

laterality	n	Percentage
allograft left	2	8.3
allograft right	2	8.3
bilateral	2	8.3
left	10	41.7
right	8	33.3

# Laterality visualization
ggplot(laterality_table, aes(x = reorder(laterality, n), y = n)) +
  geom_col(fill = "#2F4F4F", alpha = 0.8) +
  geom_text(aes(label = paste0(n, " (", Percentage, "%)")), 
            hjust = -0.1, size = 4) +
  coord_flip() +
  labs(
    title = "Laterality Distribution of Page Kidney",
    x = "Kidney Location",
    y = "Number of Cases"
  ) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.15)))

Laterality Patterns:

In this cohort, Page kidney most commonly involved the left kidney (10 cases, 41.7%), followed by the right kidney (8 cases, 33.3%). Bilateral involvement was seen in 2 cases. Notably, allograft-related Page kidney occurred in a total of 4 cases, split between left and right allografts.

Anatomical Considerations: The laterality distribution may reflect important anatomical differences between the kidneys. The left kidney sits slightly higher than the right and has a longer renal vein, while the right kidney’s proximity to the liver may influence vulnerability patterns in trauma. In transplant cases, allograft anatomy and surgical positioning factors may contribute to the observed distribution patterns.

Treatment Modalities and Management

treatment_categories <- data.frame(
  Category = c("Conservative Management", "Percutaneous/IR Drainage", "Urologic Interventions", 
               "Surgical Decompression", "Nephrectomy", "Embolization/Vascular"),
  Definition = c("Medical management only, no procedural interventions",
                "US- or CT-guided drainage, non-surgical",
                "Involving stents or urinary diversion",
                "Operative evacuation of hematoma without nephrectomy",
                "Total or partial nephrectomy (open/lap)",
                "IR embolization or stenting of vessels"),
  Examples = c("conservative, lisinopril, clonidine, captopril, propranolol",
              "US drainage, CT perc drain, IR drainage, tPA, fibrinolysis",
              "ureteral stent, ureteral stet, labetalol + stent",
              "surgical evacuation, laparoscopic fenestration, decortication, renorrhaphy",
              "lap nephrectomy, total nephrectomy, surgical evacuation + nephrectomy",
              "IR coil embolization, splenic vein stent")
)

treatment_categories %>%
  kbl(caption = "Treatment Category Definitions and Examples") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
                full_width = TRUE) %>%
  column_spec(2, width = "35%") %>%
  column_spec(3, width = "35%")

Treatment Category Definitions and Examples

Category	Definition	Examples
Conservative Management	Medical management only, no procedural interventions	conservative, lisinopril, clonidine, captopril, propranolol
Percutaneous/IR Drainage	US- or CT-guided drainage, non-surgical	US drainage, CT perc drain, IR drainage, tPA, fibrinolysis
Urologic Interventions	Involving stents or urinary diversion	ureteral stent, ureteral stet, labetalol + stent
Surgical Decompression	Operative evacuation of hematoma without nephrectomy	surgical evacuation, laparoscopic fenestration, decortication, renorrhaphy
Nephrectomy	Total or partial nephrectomy (open/lap)	lap nephrectomy, total nephrectomy, surgical evacuation + nephrectomy
Embolization/Vascular	IR embolization or stenting of vessels	IR coil embolization, splenic vein stent

df <- df %>%
  mutate(treatment_lower = tolower(treatment))

df_flags <- df %>%
  mutate(
    Conservative = str_detect(treatment_lower, "conservative|lisinopril|clonidine|captopril|amlodipine|propran|methyldopa|hctz|hydralazine|felodipine|labetolol|nitroprusside") &
                   !str_detect(treatment_lower, "drain|nephrectomy|evacuation|decortication|fenestration|stent|embol|coil|surgery|lap|ureter"),

    Perc_IR = str_detect(treatment_lower, "drain|fibrinolysis|fine needle|tp[a]?") &
              !str_detect(treatment_lower, "surgery|nephrectomy"),

    Urologic = str_detect(treatment_lower, "ureteral stent|stet"),

    Surgical_Decompression = str_detect(treatment_lower, "surgical evacuation|decortication|renorrhaphy|laparotomy|debridement|fenestration") &
               !str_detect(treatment_lower, "nephrectomy"),

    Nephrectomy = str_detect(treatment_lower, "nephrectomy"),

    Vascular = str_detect(treatment_lower, 
                          "embolization|coil|splenic vein stent")
  )

# Pivot longer to get counts
treatment_summary <- df_flags %>%
  select(reference, starts_with(c("Conservative", "Perc_IR", "Urologic", 
                                  "Surgical_Decompression", "Nephrectomy", 
                                  "Vascular"))) %>%
  pivot_longer(cols = -reference, names_to = "Category", 
               values_to = "Present") %>%
  filter(Present) %>%
  count(Category, name = "n") %>%
  mutate(Percentage = round(100 * n / nrow(df), 1))

treatment_summary %>%
  kbl(caption = "Distribution of Treatment Modalities",
      col.names = c("Treatment Category", "n", "Percentage (%)"),
      align = "lcc") %>%
  kable_styling(bootstrap_options = c("striped", "hover"),
                full_width = FALSE)

Distribution of Treatment Modalities

Treatment Category	n	Percentage (%)
Conservative	3	12.5
Nephrectomy	5	20.8
Perc_IR	6	25.0
Surgical_Decompression	9	37.5
Urologic	2	8.3
Vascular	2	8.3

# Treatment distribution plot
ggplot(treatment_summary, aes(x = reorder(Category, -n), y = n)) +
  geom_bar(stat = "identity", fill = "#DAA520") +
  geom_text(aes(label = paste0(n, " (", Percentage, "%)")), 
            vjust = -0.5, size = 4) +
  labs(
    title = "Distribution of Treatment Modalities in Page Kidney",
    x = "Treatment Category",
    y = "Number of Patients"
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.15)))

Treatment Approaches:

Treatment modalities varied considerably across the cohort. The most frequently employed approach was Surgical_Decompression (9 cases, 37.5%), followed by Perc_IR (6 cases, 25%).

The diversity in treatment approaches reflects the evolving management strategies for Page kidney, with considerations including severity of presentation, patient comorbidities, institutional expertise, and response to initial conservative measures.

Clinical Outcomes and Follow-up

df$fu.y <- as.numeric(df$fu.y)

# Summary stats
followup_summary <- df %>%
  summarize(
    `Number with Reported Follow-Up` = sum(!is.na(fu.y)),
    `Mean (years)` = round(mean(fu.y, na.rm = TRUE), 2),
    `Median (years)` = round(median(fu.y, na.rm = TRUE), 2),
    `Min (years)` = round(min(fu.y, na.rm = TRUE), 2),
    `Max (years)` = round(max(fu.y, na.rm = TRUE), 2)
  )

followup_summary %>%
  kbl(caption = "Follow-up Duration Summary",
      align = "lc") %>%
  kable_styling(bootstrap_options = c("striped", "hover"),
                full_width = FALSE)

Follow-up Duration Summary

Number with Reported Follow-Up	Mean (years)	Median (years)	Min (years)	Max (years)
20	1.1	0.38	0.01	9

ggplot(df %>% filter(!is.na(fu.y)), aes(x = fu.y)) +
  geom_histogram(binwidth = 1, fill = "#006400", color = "black") +
  labs(
    title = "Distribution of Follow-up Duration",
    subtitle = paste0("n = ", sum(!is.na(df$fu.y)), " patients with reported follow-up"),
    x = "Years of Follow-Up",
    y = "Number of Patients"
  )

# Survival/outcome summary
survival_summary <- data.frame(
  Outcome = c("Total cases in study", "Cases with follow-up data", 
              "Documented survival", "Reported mortality"),
  Count = c(nrow(df), sum(!is.na(df$fu.y)), 
            sum(df$fu.status == TRUE, na.rm = TRUE), 
            sum(df$fu.status == FALSE, na.rm = TRUE)),
  Percentage = c(100, round(100 * sum(!is.na(df$fu.y)) / nrow(df), 1),
                round(100 * sum(df$fu.status == TRUE, 
                                na.rm = TRUE) / sum(!is.na(df$fu.y)), 1),
                0)
)

survival_summary %>%
  kbl(caption = "Clinical Outcomes Summary",
      align = "lcc") %>%
  kable_styling(bootstrap_options = c("striped", "hover"),
                full_width = FALSE)

Clinical Outcomes Summary

Outcome	Count	Percentage
Total cases in study	24	100
Cases with follow-up data	20	83
Documented survival	23	115
Reported mortality	0	0

Follow-up and Clinical Outcomes:

Among the reported cases, no mortality was observed, and all patients survived through their respective follow-up durations. Follow-up data were available for 20 out of 24 cases (83.3%).

The average duration of follow-up was 1.1 years (median: 0.4 years), ranging from 0 to 9 years.

While the absence of mortality is encouraging, the lack of standardized long-term outcome reporting (e.g., blood pressure control, renal function) limits broader conclusions. Nevertheless, these data suggest that with timely recognition and appropriate intervention, short- to mid-term prognosis for Page kidney is excellent.

Data Export and Workspace Management

pmh_cleaned <- pmh_all %>%
  group_by(id) %>%
  summarise(
    pmh = paste(unique(category), collapse = "; "),
    .groups = "drop"
  )

ss_cleaned <- ss_clean %>%
  group_by(id) %>%
  summarise(
    ss = paste(unique(symptom), collapse = "; "),
    .groups = "drop"
  )

# Select only the treatment flag columns
treatment_cols <- c("Conservative", "Perc_IR", "Urologic", 
                    "Surgical_Decompression", "Nephrectomy", "Vascular")

# Create a treatment summary
treatment_cleaned <- df_flags %>%
  mutate(id = row_number()) %>%
  pivot_longer(
    cols = all_of(treatment_cols),
    names_to = "category",
    values_to = "present"
  ) %>%
  filter(present) %>%
  group_by(id) %>%
  summarise(
    treatment_summary = paste(category, collapse = "; "),
    .groups = "drop"
  )

df <- df %>%
  mutate(id = row_number()) %>%
  left_join(pmh_cleaned, by = "id") %>%
  left_join(ss_cleaned, by = "id") %>%
  left_join(treatment_cleaned, by = "id")

# Making final table
df <- df %>%
  select(
    reference, year, age, gender,
    pmh = pmh.y,
    onset.y, mechanism,
    ss = ss.y,
    hypertension,
    ua = ua_result,
    grade, size.cm, page.type, laterality,
    treatment = treatment_summary,
    fu.status, fu.y
  )

# Export cleaned data
write.csv(df, "data/tpk-table.csv", row.names = FALSE)

# Cleaning workspace
rm(list = setdiff(ls(), "df"))

Table 1

# Clean and format the data for Table 1, using "NR" for missing data
table1 <- df %>%
  mutate(
    # Clean reference formatting
    Reference = gsub("_", " et al., ", reference),

    # Format year
    Year = year,

    # Format age
    Age = ifelse(is.na(age) | age == "NA" | age == "Not reported", "NR", age),

    # Clean gender
    Gender = case_when(
      gender == "m" ~ "Male",
      gender == "f" ~ "Female",
      is.na(gender) | gender == "NA" | gender == "Not reported" ~ "NR",
      TRUE ~ gender
    ),

    # Clean past medical history
    `Past Medical History` = ifelse(is.na(pmh) | pmh == "NA" | pmh == "Not reported", "NR", pmh),

    # Format onset time with day-level resolution for very short intervals
    `Time to Onset` = case_when(
      is.na(onset.y) | onset.y == "NA" | onset.y == "Not reported" ~ "NR",
      onset.y < (1/12) ~ paste0(round(onset.y * 365), " days"),
      onset.y < 1 ~ paste0(round(onset.y * 12, 1), " months"),
      onset.y == 1 ~ "1 year",
      onset.y > 1 ~ paste0(round(onset.y, 1), " years"),
      TRUE ~ "NR"
    ),

    # Clean mechanism
    `Injury Mechanism` = case_when(
      is.na(mechanism) | mechanism == "NA" | mechanism == "Not reported" ~ "NR",
      mechanism == "mvc" ~ "Motor vehicle collision",
      mechanism == "fall" ~ "Fall",
      mechanism == "blunt" ~ "Blunt trauma",
      TRUE ~ mechanism
    ),

    # Clean signs and symptoms
    `Signs & Symptoms` = ifelse(is.na(ss) | ss == "NA" | ss == "Not reported", "NR", ss),

    # Format hypertension
    Hypertension = case_when(
      hypertension == TRUE ~ "Yes",
      hypertension == FALSE ~ "No",
      is.na(hypertension) | hypertension == "NA" | hypertension == "Not reported" ~ "NR",
      TRUE ~ as.character(hypertension)
    ),

    # Clean urinalysis
    Urinalysis = case_when(
      is.na(ua) | ua == "NA" | ua == "Not reported" ~ "NR",
      ua == "Positive" ~ "Abnormal",
      ua == "Negative" ~ "Normal",
      TRUE ~ ua
    ),

    # Clean injury grade
    `Injury Grade` = ifelse(is.na(grade) | grade == "NA" | grade == "Not reported", "NR", grade),

    # Format size
    `Size (cm)` = case_when(
      is.na(size.cm) | size.cm == "NA" | size.cm == "Not reported" ~ "NR",
      TRUE ~ paste0(size.cm, " cm")
    ),

    # Clean page kidney type
    `Page Kidney Type` = case_when(
      is.na(page.type) | page.type == "NA" | page.type == "Not reported" ~ "NR",
      page.type == "hematoma" ~ "Hematoma",
      page.type == "urinoma" ~ "Urinoma",
      page.type == "lymphocele" ~ "Lymphocele",
      TRUE ~ page.type
    ),

    # Clean laterality
    Laterality = case_when(
      is.na(laterality) | laterality == "NA" | laterality == "Not reported" ~ "NR",
      laterality == "left" ~ "Left",
      laterality == "right" ~ "Right",
      laterality == "bilateral" ~ "Bilateral",
      laterality == "allograft right" ~ "Allograft (Right)",
      laterality == "allograft left" ~ "Allograft (Left)",
      TRUE ~ laterality
    ),

    # Clean treatment
    Treatment = case_when(
      is.na(treatment) | treatment == "NA" | treatment == "Not reported" ~ "NR",
      treatment == "Conservative" ~ "Medical management",
      treatment == "Surgical_Decompression" ~ "Surgical decompression",
      treatment == "Perc_IR" ~ "Percutaneous intervention",
      treatment == "Perc_IR; Surgical_Decompression" ~ "Percutaneous intervention + Surgical decompression",
      treatment == "Perc_IR; Vascular" ~ "Percutaneous intervention + Vascular intervention",
      treatment == "Urologic" ~ "Urological intervention",
      treatment == "Nephrectomy" ~ "Nephrectomy",
      treatment == "Vascular" ~ "Vascular intervention",
      TRUE ~ treatment
    ),

    # Clean follow-up status
    `Follow-up Status` = case_when(
      fu.status == TRUE ~ "Alive",
      fu.status == FALSE ~ "NR",
      is.na(fu.status) | fu.status == "NA" | fu.status == "Not reported" ~ "NR",
      TRUE ~ "NR"
    ),

    # Format follow-up duration
    `Follow-up Duration` = case_when(
      is.na(fu.y) | fu.y == "NA" | fu.y == "Not reported" ~ "NR",
      fu.y < 1 ~ paste0(round(fu.y * 12, 1), " months"),
      fu.y == 1 ~ "1 year",
      fu.y > 1 ~ paste0(round(fu.y, 1), " years"),
      TRUE ~ "NR"
    )
  ) %>%
  # Select and reorder columns for final table
  select(
    Reference,
    Year,
    Age,
    Gender,
    `Past Medical History`,
    `Time to Onset`,
    `Injury Mechanism`,
    `Signs & Symptoms`,
    Hypertension,
    Urinalysis,
    `Injury Grade`,
    `Size (cm)`,
    `Page Kidney Type`,
    Laterality,
    Treatment,
    `Follow-up Status`,
    `Follow-up Duration`
  )

# Display the cleaned table
kable(table1,
      caption = "Table 1. Characteristics of Page Kidney Cases in Systematic Review. Data presented as reported in original publications. 'NR' indicates data not reported in the original study. Time to onset refers to time from initial trauma to Page kidney diagnosis. Injury grade refers to renal trauma grading when available. Treatment categories: Medical management includes antihypertensive therapy and monitoring; Percutaneous intervention includes drainage, embolization, or stenting; Surgical decompression includes open or laparoscopic capsulotomy; Urological intervention includes ureteral stenting or nephrostomy; Vascular intervention includes angioplasty or bypass procedures.",
      format = "html",
      na = "NR",
      table.attr = "style='font-size:12.5px'") %>%
  kable_styling(
    bootstrap_options = c("striped", "hover", "condensed", "responsive"),
    full_width = FALSE,
    position = "left"
  ) %>%
  column_spec(1, bold = TRUE, width = "12em") %>%  # Reference column
  column_spec(5, width = "15em") %>%  # Past Medical History
  column_spec(8, width = "15em") %>%  # Signs & Symptoms
  column_spec(15, width = "12em")     # Treatment

Table 1. Characteristics of Page Kidney Cases in Systematic Review. Data presented as reported in original publications. ‘NR’ indicates data not reported in the original study. Time to onset refers to time from initial trauma to Page kidney diagnosis. Injury grade refers to renal trauma grading when available. Treatment categories: Medical management includes antihypertensive therapy and monitoring; Percutaneous intervention includes drainage, embolization, or stenting; Surgical decompression includes open or laparoscopic capsulotomy; Urological intervention includes ureteral stenting or nephrostomy; Vascular intervention includes angioplasty or bypass procedures.

Reference	Year	Age	Gender	Past Medical History	Time to Onset	Injury Mechanism	Signs & Symptoms	Hypertension	Urinalysis	Injury Grade	Size (cm)	Page Kidney Type	Laterality	Treatment	Follow-up Status	Follow-up Duration
Aragona et al., 1991	1991	25	Male	NR	9 years	Motor vehicle collision	Asymptomatic; Hypertension	Yes	Normal	NR	6.6 cm	Hematoma	Left	Surgical decompression	Alive	3 years
Babel et al., 2010	2010	89	Male	Hypertension; Diabetes; Chronic renal insufficiency; Coronary artery disease	3 months	Fall	NR	Yes	NR	NR	15 cm	Hematoma	Left	Medical management	Alive	9 months
Boggi et al., 1998	1998	15	Male	Congenital kidney anomaly	4 months	Blunt trauma	NR	Yes	NR	NR	NR	Hematoma	Right	Percutaneous intervention	Alive	9 years
Kumar et al., 2015	2015	66	Male	Renal transplant; Diabetes; Neuropathy	3 days	Fall	Chest Pain; Hypertension	Yes	Normal	NR	11 cm	Hematoma	Allograft (Right)	Percutaneous intervention + Surgical decompression	Alive	0.1 months
Lin et al., 2020	2019	46	Female	NR	2 months	Blunt trauma	NR	Yes	NR	NR	15 cm	Hematoma	Left	Percutaneous intervention	Alive	3 months
Marcou et al., 2021	2021	16	Male	NR	6 days	Motor vehicle collision	NR	Yes	NR	3	NR	Hematoma	Right	Urological intervention	Alive	5 months
Massumi et al., 1969	1969	16	Male	NR	3 days	Fall	Hematuria; Flank pain; Hypertension	Yes	Abnormal	NR	NR	Hematoma	Right	Medical management	Alive	6 months
Matlaga et al., 2002	2002	16	Male	NR	NR	Blunt trauma	Flank pain; Hypertension; Hematuria	Yes	Abnormal	NR	NR	Urinoma	Bilateral	Urological intervention	Alive	4 months
Mattson et al., 2021	2021	31	Male	Seizure disorder	14 days	Blunt trauma	NR	No	NR	NR	NR	Hematoma	Left	Percutaneous intervention + Vascular intervention	Alive	1 months
Minagawa et al., 2009	2009	17	Male	NR	29 days	Blunt trauma	Flank pain; Hematuria; Hypertension	Yes	Abnormal	NR	NR	Lymphocele	Right	Percutaneous intervention + Surgical decompression	Alive	1.5 years
Moriarty et al., 1997	1997	16	Male	NR	1 year	Motor vehicle collision	NR	Yes	NR	NR	7 cm	Hematoma	Left	Surgical decompression	Alive	3.2 years
Mullins et al., 1975	1975	17	Male	NR	1 year	Blunt trauma	Headache; Back pain; Flank pain; Fatigue; Hypertension	Yes	Normal	NR	11 cm	Hematoma	Right	Nephrectomy	NR	NR
Murray et al., 2010	2010	48	Male	NR	0 days	Fall	Flank pain; Hematuria; Hypertension	Yes	Abnormal	NR	NR	Hematoma	Left	Nephrectomy	Alive	NR
Myrianthefs et al., 2007	2007	24	Male	NR	8 days	Motor vehicle collision	Abdominal pain; Abdominal distension; Hematuria	No	Abnormal	NR	NR	NR	Right	Medical management	Alive	1 year
Oliveira et al., 2003	2003	23	Male	NR	3 years	Blunt trauma	Flank pain; Ecchymosis; Hypertension	Yes	Normal	NR	7.5 cm	Hematoma	Left	Nephrectomy	Alive	1 year
Pan et al., 2020	2020	34	Male	Diabetes; Renal transplant; Pancreas transplant	30 days	Blunt trauma	NR	Yes	NR	NR	7.4 cm	Hematoma	Allograft (Left)	Surgical decompression	Alive	1 year
Sedigh et al., 2017	2017	67	Male	Renal transplant	15 days	Blunt trauma	NR	No	NR	NR	4 cm	Hematoma	Allograft (Right)	Surgical decompression	Alive	0.3 months
Sokhal et al., 2018	2018	24	Male	NR	6 years	Motor vehicle collision	NR	Yes	NR	NR	18 cm	Hematoma	Left	Nephrectomy	Alive	0.2 months
Sokhal et al., 2018	2018	21	Male	NR	2 years	Fall	Flank pain; Palpitations; Headache; Hypertension	Yes	Normal	NR	12 cm	Hematoma	Right	Nephrectomy	Alive	3 months
Takahashi et al., 2017	2017	67	Male	Renal transplant	7 days	Blunt trauma	Abdominal pain; Hypertension	Yes	Normal	1	12 cm	Hematoma	Allograft (Left)	Surgical decompression	Alive	NR
Tuong et al., 2016	2016	9	Male	Congenital kidney anomaly	6 days	Blunt trauma	Abdominal pain; Hematuria; Hypertension	Yes	Abnormal	2	NR	Hematoma	Left	Surgical decompression	Alive	1 months
Reference	2023	54	Male	NR	4 days	Motor vehicle collision	Back pain; Abdominal pain; Hypertension; Hematuria	Yes	Abnormal	NR	NR	Hematoma	Bilateral	Surgical decompression	Alive	2 months
Koyanagawa et al., 2024	2024	26	Male	Asthma; Fracture; Substance use	1 days	Blunt trauma	Flank pain; Vomiting; Hypertension	Yes	Abnormal	NR	12 cm	Hematoma	Right	Vascular intervention	Alive	NR
Lind et al., 2024	2024	54	Male	Sleep apnea; Hypertension	1 days	Fall	NR	Yes	NR	NR	NR	Hematoma	Left	Percutaneous intervention	Alive	0.4 months

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